Problems in digital public services Personal proposal by Per-Arne Andersen D6 · Spring 2027 https://thesis.uya.no/proposals/spot-the-digital-roadblocks/ Group feedback about a digital service into specific problems the service owner can fix. TECHNICAL - Create a labelled feedback set for an appointment-booking form. - Build TF–IDF clustering and a view linking each group to the original comments and form step. DATA (planned) 200 fictional comments + 40 pilot comments Five booking steps; 180 single-topic and 20 mixed/unclear evaluation comments. Label step and actionable problem separately. Vary vocabulary, tone and length independently of labels. METHOD Fix TF–IDF and keyword settings on pilot data. Score step clustering on unambiguous comments and audit missed actionable problems. Six IS students triage 20 disjoint comments across views; measure corrections, missed issues and time. OUTPUT Feedback dashboard, labelled comments and grouping results. BACHELOR Task: Build a TF–IDF comment classifier with a manual correction screen. Data: 200 fictional comments + 40 pilot comments (planned) Five booking steps; 180 single-topic and 20 mixed/unclear evaluation comments. Label step and actionable problem separately. Vary vocabulary, tone and length independently of labels. Requires: Independent label checking and IS students; all comments must be authored. Method: Use the labelled synthetic comments; keep development/evaluation sets separate and report classification errors and correction effort. Plan 4 participant sessions. Output: A reproducible triage prototype and error report. Use relevant literature to justify the established approach; a new research contribution is not the aim of this proposal. MASTER Investigate how ambiguous feedback changes human-in-the-loop triage. Compare automatic labels and correction behaviour on single/mixed-problem comments; analyse whether correction restores meaningful service categories. Intended contribution: Design principles for reviewable feedback classification under ambiguity. STARTING PAPERS Amershi et al. (2019) — Guidelines for Human-AI Interaction: https://www.microsoft.com/en-us/research/wp-content/uploads/2019/01/Guidelines-for-Human-AI-Interaction-camera-ready.pdf Select and justify the correction, explanation or uncertainty design being tested. Wang & Strong (1996) — Beyond Accuracy: https://www.tandfonline.com/doi/abs/10.1080/07421222.1996.11518099 Distinguish accuracy, completeness, representation and fitness for the task. Search Scopus or Web of Science and ACM Digital Library using the topic query, then follow citations to the thesis start date. Record searches and compare methods, data, findings and limitations in literature-matrix.csv. Use that review to confirm or revise the gap and choose a current comparator. The linked papers are starting points. START HERE Tools: Python, pandas, scikit-learn, Streamlit 1. Write 10 comments across the five form steps and label the underlying problem separately. 2. Define label rules and split development/evaluation comments before choosing clustering settings. 3. Run keyword grouping first; inspect ambiguous comments before creating the full set. Literature search: digital public service feedback analysis usability barriers Study controls: - Keep paraphrase families in one partition; do not inspect evaluation labels while choosing settings. - Students represent novice analysts; synthetic comments do not establish performance on a real municipal service. - Use TF–IDF word unigrams/bigrams and KMeans with k=5, n_init=20 and random_state=42. Finalise vocabulary and keyword rules on pilot material only. - Before collecting participant data, agree consent, storage and withdrawal handling with the supervisor. Use participant codes, not names, in study files. - Separate silent timed tasks from retrospective interviews. Timing during think-aloud sessions is descriptive, not an isolated interface-speed effect. - For the master’s study, use the research task above to define the factors and comparisons in this pilot plan. Preregister one primary outcome and feasible scope after the literature review; do not add every possible model or interface variant. REQUIRES Independent label checking and 6 IS students; all comments must be authored. -------------------- NON-TECHNICAL - Prepare paper screenshots of an appointment-booking flow; no interactive prototype. - Ask users to explain their next action when booking, changing or cancelling an appointment. DATA (planned) 6 paper task sequences + 12 adult sessions Draw screenshots for login, date selection, required fields, confirmation and cancellation. Use fictional names and appointments; include 2 deliberately confusing labels. METHOD Paper walkthroughs; record chosen actions and misunderstood labels. Code problems by step and severity. Treat findings as comprehension evidence, not measured usability of a working service. OUTPUT Ranked wording/navigation problems and revised paper screens. BACHELOR Task: Find common usability obstacles in the proposed public-service screens. Data: 6 paper task sequences (planned) Draw screenshots for login, date selection, required fields, confirmation and cancellation. Use fictional names and appointments; include 2 deliberately confusing labels. Requires: Recruit adults; the mock-up and scripts must be created. Method: Use paper screens in task sessions; group observed problems and rank them by consequence. Plan 4 participant sessions. Output: A prioritised issue list and revised paper screens. Use relevant literature to justify the established approach; a new research contribution is not the aim of this proposal. MASTER Explain why one interface problem blocks some public-service tasks but not others. Compare observed failures by task demands and information quality; seek alternative explanations in prior service familiarity. Intended contribution: A bounded explanatory account of digital-service obstacles, not only an issue inventory. STARTING PAPERS Vessey & Galletta (1991) — Cognitive Fit: https://pubsonline.informs.org/doi/10.1287/isre.2.1.63 Test whether a representation helps one kind of task more than another. Wang & Strong (1996) — Beyond Accuracy: https://www.tandfonline.com/doi/abs/10.1080/07421222.1996.11518099 Distinguish accuracy, completeness, representation and fitness for the task. Search Scopus or Web of Science and ACM Digital Library using the topic query, then follow citations to the thesis start date. Record searches and compare methods, data, findings and limitations in literature-matrix.csv. Use that review to confirm or revise the gap and choose a current comparator. The linked papers are starting points. START HERE Tools: LibreOffice Writer/Calc, audio recorder with consent; no programming required. 1. Prepare a pilot with 2 examples from: 6 scripted tasks + 12 adult user sessions. Write the task questions and a reference answer sheet. 2. Write a recruitment message, information sheet and consent form for the participants named above. Agree privacy handling with the supervisor before contact. 3. Pilot one session after approval; revise unclear questions, freeze the task sets and coding categories, then recruit the planned sample. Literature search: digital public service feedback analysis usability barriers qualitative scenario study Study controls: - Before collecting participant data, agree consent, storage and withdrawal handling with the supervisor. Use participant codes, not names, in study files. - Pilot separately, then freeze the questions and coding plan. Check objective answer keys independently; keep an audit trail of coding, including disagreements. - For comparisons, counterbalance order and case assignment; do not show a person both versions of one case. Report participant-level findings, not repeated tasks as independent people. REQUIRES Recruit adults; the mock-up and scripts must be created. Bachelor: apply established methods and evaluate a practical solution or study. Master: position a research question in current scientific literature, investigate a mechanism or unresolved problem, and explain the contribution. Final scope is agreed with me. Study sizes are proposed; participant recruitment and planned materials are not already arranged.